Algorithms designed to solve dynamic multi-objective optimization problems (DMOPs) need to consider all of themultiple conflicting objectives to determine the optimal solutions. However, objective functions, constraints orparameters can change over time, which presents a considerable challenge. Algorithms should be able not only toidentify the optimal solution but also to quickly detect and respond to any changes of environment. In order toenhance the capability of detection and response to environmental changes, we propose a dynamic multiobjectiveoptimization (DMOO) algorithm based on the detection of environment change intensity andensemble learning (DMOO-DECI&EL). First, we propose a method for detecting environmental change intensity,where the change intensity is quantified and used to design response strategies. Second, a series of responsestrategies under the framework of ensemble learning are given to handle complex environmental changes.Finally, a boundary learning method is introduced to enhance the diversity and uniformity of the solutions.Experimental results on 14 benchmark functions demonstrate that the proposed DMOO-DECI&EL algorithmachieves the best comprehensive performance across three evaluation criteria, which indicates that DMOODECI&EL has better robustness and convergence and can generate solutions with better diversity compared tofive other state-of-the-art dynamic prediction strategies. In addition, the application of DMOO-DECI&EL to thereal-world scenario, namely the economic power dispatch problem, shows that the proposed method caneffectively handle real-world DMOPs.